VideoCube Evaluation Server (R-OPE Mechanism)

Submit your tracking results on VideoCube to this website and evaluate the performance. Leaderboard will be updated immediately once the results are evaluated.


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Titan RTX





LaTex Bibtex:

@InProceedings{10.1007/978-3-319-48881-3_56, author="Bertinetto, Luca and Valmadre, Jack and Henriques, Jo{\~a}o F. and Vedaldi, Andrea and Torr, Philip H. S.", editor="Hua, Gang and J{\'e}gou, Herv{\'e}", title="Fully-Convolutional Siamese Networks for Object Tracking", booktitle="Computer Vision -- ECCV 2016 Workshops", year="2016", publisher="Springer International Publishing", address="Cham", pages="850--865", abstract="The problem of arbitrary object tracking has traditionally been tackled by learning a model of the object's appearance exclusively online, using as sole training data the video itself. Despite the success of these methods, their online-only approach inherently limits the richness of the model they can learn. Recently, several attempts have been made to exploit the expressive power of deep convolutional networks. However, when the object to track is not known beforehand, it is necessary to perform Stochastic Gradient Descent online to adapt the weights of the network, severely compromising the speed of the system. In this paper we equip a basic tracking algorithm with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video. Our tracker operates at frame-rates beyond real-time and, despite its extreme simplicity, achieves state-of-the-art performance in multiple benchmarks.", isbn="978-3-319-48881-3" }

Submissions (R-OPE Mechanism)

Submmition Count:

2 submissions.


Method N-PRE PRE SRIoU SRDIoU SRGIoU Robust Hardware Language Date(UTC) Results Reports
1 SiamFC 0.545 0.281 0.371 0.364 0.360 0.727 Titan RTX python 2022-03-03, 09:14:22 reports.json
2 SiamFC 0.545 0.281 0.371 0.364 0.360 0.727 Titan RTX python 2022-03-02, 10:08:30 - reports.json